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Male and female politicians on Twitter: A machine learning approach

Published online by Cambridge University Press:  02 January 2026

Javier Beltran ,
Aina Gallego ,
Alba Huidobro ,
Enrique Romero  and
Lluís Padró
Javier Beltran
Affiliation:
Department of Linguistics, Universitat de Barcelona, Barcelona, Spain
Aina Gallego*
Affiliation:
Institut Barcelona d'Estudis Internationals and Institute of Political Economy and Governance, Barcelona, Spain
Alba Huidobro
Affiliation:
Institute Barcelona d'Estudis Internationals and Universitat Pompeu Fabra, Barcelona, Spain
Enrique Romero
Affiliation:
Computer Science Department, Universitat Politecnica de Catalunya, Barcelona, Spain
Lluís Padró
Affiliation:
Computer Science Department, Universitat Politecnica de Catalunya, Barcelona, Spain
*
Address for correspondence: Aina Gallego, Institut Barcelona d'Estudis Internationals and Institute of Political Economy and Governance, Barcelona, Spain. Email: agallego@ibei.org
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Abstract

How does the language of male and female politicians differ when they communicate directly with the public on social media? Do citizens address them differently? We apply Lasso logistic regression models to identify the linguistic features that most differentiate the language used by or addressed to male and female Spanish politicians. Male politicians use more words related to politics, sports, ideology and infrastructure, while female politicians talk about gender and social affairs. The choice of emojis varies greatly across genders. In a novel analysis of tweets written by citizens, we find evidence of gender‐specific insults, and note that mentions of physical appearance and infantilising words are disproportionately found in text addressed to female politicians. The results suggest that politicians conform to gender stereotypes online and reveal ways in which citizens treat politicians differently depending on their gender.

Keywords

Twittergender differencespoliticiansmachine learningsocial media

Information

Type
Research Notes
Information
European Journal of Political Research , Volume 60 , Issue 1 , February 2021 , pp. 239 - 251
Copyright
Copyright © 2020 European Consortium for Political Research

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References

Aaldering, L. & Van Der Pas, D.J. (2018). Political leadership in the media: Gender bias in leader stereotypes during campaign and routine times. British Journal of Political Science 1–21. https://doi.org/10.1017/S0007123417000795Google Scholar
Bailey, M.B. & Nawara, S.P. (2018). Addressing women and minorities on social media by the 2016 U.S. presidential candidates. Social Science Computer Review 37. https://doi.org/10.1177/0894439318779342Google Scholar
Barberá, P. (2015). Birds of the same feather tweet together: Bayesian ideal point estimation using twitter data. Political Analysis 23(1): 76–91.CrossRefGoogle Scholar
Barberá, P., Jost, J.T., Nagler, J., Tucker, J.A. & Bonneau, R. (2015). Tweeting from left to right: Is online political communication more than an echo chamber? Psychological Science 26(10): 1531–1542.CrossRefGoogle ScholarPubMed
Bauer, N.M. (2015). Emotional, sensitive, and unfit for office? Gender stereotype activation and support female candidates. Political Psychology 36(6): 691–708.CrossRefGoogle Scholar
Bauer, N.M. (2018). Running local: Gender Stereotyping and female candidates in local elections. Urban Affairs Review. https://doi.org/10.1177/1078087418770807Google Scholar
Beauchamp, N. (2017). Predicting and interpolating state‐level polls using twitter textual data. American Journal of Political Science 61(2): 490–503.CrossRefGoogle Scholar
Bohren, J.A., Imas, A. & Rosenberg, M. (2018). The dynamics of discrimination: Theory and evidence. PIER Working Paper. 18‐016. http://doi.org/10.2139/ssrn.3235376CrossRefGoogle Scholar
Bystrom Theocharis, Y., Barberá, P., Fazekas, Z., Popa, S.A. & Parnet, O. (2016). A bad workman blames his tweets: The consequences of citizens uncivil twitter use when interacting with party candidates. Journal of Communication 66(6): 1007–1031.CrossRefGoogle Scholar
Cassese, E.C. & Holman, M.R. (2018). Party and gender stereotypes in campaign attacks. Political Behavior 40(3): 785–807.CrossRefGoogle Scholar
Chen, Z., Lu, X., Shen, S., Ai, W., Liu, X. & Mei, Q. (2018). Through a gender lens: An empirical study of emoji usage over large‐scale android users. In WWW 2018: The 2018 Web Conference, April 23–27, 2018, Lyon, France. https://doi.org/10.1145/3178876.CrossRefGoogle Scholar
Ditonto, T.M., Hamilton, A.J. & Redlawsk, D.P. (2014). Gender stereotypes, Information search, and voting behavior in political campaigns. Political Behavior 36(2): 335–358.CrossRefGoogle Scholar
Dolan, K. & Lynch, T. (2014). It takes a survey: Understanding gender stereotypes, abstract attitudes, and voting for women candidates. American Politics Research 42(4): 656–676.CrossRefGoogle Scholar
Evans, H.K. & Clark, J.H. (2016). “You tweet like a girl!” How female candidates campaign on Twitter. American Politics Research 44(2): 326–352.CrossRefGoogle Scholar
Grimmer, J. & Stewart, B.M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis 21(03): 267–297.CrossRefGoogle Scholar
Herrnson, P.S., Lay, J.C. & Stokes, A.K. (2003). Women running “as Women”: Candidate gender, campaign issues, and voter‐targeting strategies. The Journal of Politics 65(1): 244–255.CrossRefGoogle Scholar
Hwang, H.S. (2014). Gender differences in emoticon use on mobile text messaging: Evidence from a Korean sample. International Journal of Journalism & Mass Communication 21. http://doi.org/10.15344/2349-2635/2014/107Google Scholar
Jamieson, K.H. (1995). Beyond the double bind: Women and leadership. New York: Oxford University Press.CrossRefGoogle Scholar
Jost, J.T., Barberá, P., Bonneau, R., Langer, M., Metzger, M., Nagler, J., Sterling, J. & Tucker, J.A. (2018). How social media facilitates political protest: Information, motivation, and social networks. Political Psychology 39(S1): 85–118.CrossRefGoogle Scholar
Just, M.R. & Crigler, A.N. (2014). Gender and self‐presentation in social media: An analysis of the 10 most competitive 2012 U.S. senate races. APSA 2014 Annual Meeting Paper. Available at: https://papers.ssrn.com/abstract=2453741Google Scholar
Krook, M.L. (2017). Violence against women in politics. Journal of Democracy 28(1): 74–88.CrossRefGoogle Scholar
Lawless, J.L. (2004). Women, war, and winning elections: Gender stereotyping in the post‐September 11th era. Political Research Quarterly 57(3): 479–490.CrossRefGoogle Scholar
Lawless, J.L. (2015). Female candidates and legislators. Annual Review of Political Science 18(1): 349–366.CrossRefGoogle Scholar
Lucas, C., Nielsen, R.A., Roberts, M.E., Stewart, B.M., Storer, A. & Tingley, D. (2015). Computer‐assisted text analysis for comparative politics. Political Analysis 23(2): 254–277.CrossRefGoogle Scholar
Munger, K. (2017). Don't @ me: Experimentally reducing partisan incivility on Twitter. Working Paper. Available at: http://cess.nyu.edu/wp-content/uploads/2017/02/Dont-@-Me.pdfGoogle Scholar
Newman, M.L., Groom, C.J., Handelman, L.D. & Pennebaker, J.W. (2008). Gender differences in language use: An analysis of 14,000 text samples. Discourse Processes 45(3): 211–236.CrossRefGoogle Scholar
Palomares, N.A. (2008). Explaining gender‐based language use: Effects of gender identity salience on references to emotion and tentative language in intra‐ and intergroup contexts. Human Communication Research 34(2): 263–286.CrossRefGoogle Scholar
Park, G., Yaden, D.B., Schwartz, H.A., Kern, M.L., Eichstaedt, J.C., Kosinski, M., Stillwell, D., Ungar, L.H. & Seligman, M.E.P. (2016). Women are warmer but no less assertive than men: Gender and language on Facebook. PLoS ONE 11(5): e0155885.CrossRefGoogle ScholarPubMed
Rheault, L., Rayment, E. & Musulan, A. (2019). Politicians in the line of fire: Incivility and the treatment of women on social media. Research & Politics 6(1): 1–7.CrossRefGoogle Scholar
Roberts, M.E., Stewart, B.M., Tingley, D., Lucas, C., Leder‐Luis, J., Gadarian, S.K., Albertson, B. & Rand, D.G. (2014). Structural topic models for open‐ended survey responses. American Journal of Political Science 58(4): 1064–1082.CrossRefGoogle Scholar
Teele, D.L., Kalla, J., & Rosenbluth, F. (2018). The ties that double bind: Social roles and women's underrepresentation in politics. American Political Science Review 112(3): 525–541.CrossRefGoogle Scholar
Wagner, K.M., Gainous, J., & Holman, M.R. (2017). I am woman, hear me tweet! Gender differences in Twitter use among congressional candidates. Journal of Women, Politics & Policy 38(4): 430–455.CrossRefGoogle Scholar
Wilkerson, J. & Casas, A. (2017). Large‐scale computerized text analysis in political science: Opportunities and challenges. Annual Review of Political Science 20(1): 529–544.CrossRefGoogle Scholar
Wolf, A. (2000). Emotional expression online: Gender differences in emoticon use. CyberPsychology & Behavior 3(5): 827–833.CrossRefGoogle Scholar
Wu, A.H. (2018). Gendered language on the economics job market rumors forum. AEA Papers and Proceedings 108: 175–179.CrossRefGoogle Scholar
Yarchi, M. & Samuel‐Azran, T. (2018). Women politicians are more engaging: Male versus female politicians’ ability to generate users’ engagement on social media during an election campaign. Information, Communication & Society 21(7): 978–995.CrossRefGoogle Scholar
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